Recommender Systems: Introduction

By Dietmar Jannach, Markus Zanker, Alexander Felfernig, G. Friedrich. 2010

1,455 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/766702f78726d49d1b2ab632291fbcb1349e102a

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Citations in the corpus

The use of machine learning algorithms in recommender systems: A systematic review (2015, neutral): “There are several synonyms for RSs. Based on [29] this systematic review considers RS terms that replace “recommender” by “recommendation”, and terms that replace “system” by “platform”, or “engine”.”
Knowledgeable Explanations for Recommender Systems (2010, background): “Recommender systems (RS) provide personalized support to online users in retrieving items from large catalogs [1].”
Recommendation systems: Principles, methods and evaluation (2015, methodology): “The similarity between two items u and v can be defined as [12,43,48] follows:”
Social Media Mining: An Introduction (2014, methodology): “Other algorithms not discussed in this chapter include graph coloring [Jensen and Toft, 1994], (quasi) clique detection [Abello, Resende, and Sudarsky, 2002], graph isomorphism [McKay, 1980], topological sort algorithms [Cormen, 2009], and the traveling salesman problem (TSP) [Cormen, 2009], among o”
Recommender Systems: Introduction and Challenges (2015, neutral): “been published as well [27].”
Learning the Preferences of Ignorant, Inconsistent Agents (2015, background): “The problem of learning a person’s preferences from observations of their choices features prominently in economics (Hausman 2011), in cognitive science (Baker, Saxe, and Tenenbaum 2011; Ullman et al. 2009), and in applied machine learning (Jannach et al. 2010; Ermon et al. 2014).”
Recommender systems: from algorithms to user experience (2012, background): “…wide range of commerce and content applications (both online and offline), where recommender systems handbooks and texts have been published (e.g., Jannach et al. 2011; Ricci et al. 2011), where universities are offering courses on recommender systems, and where there is a dedicated annual…”
Schema-summarization in linked-data-based feature selection for recommender systems (2017, background): “In this work we ﬁxed k equals to 20 which is a quite typical value in a recommendation task [10].”
Deep Learning Based Recommender System (2017, background): “rse range of auxiliary information such as texts, images and videos can be taken into account. Hybrid model refers to recommender system that integrates two or more types of recommendation strategies [8, 69]. Suppose we have M users and N items, and R denotes the interaction matrix and Rˆ denotes th”
A systematic review and taxonomy of explanations in decision support and recommender systems (2017, background): “One of the most prominent examples of ML-based applications today are the so-called recommender systems (Jannach et al. 2010).”
Music Recommendation (2016, methodology): “3: Pipelin d hybri ization design; adapted from [20].”
Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning (2017, background): “Similarity between two items i and j is calculated as follows: sim(i, j) = ∑ (ru,i − ru)(ru, j − ru) √∑ (ru,i − ru)2 √∑ (ru, j − ru)2 (1) where ru,i is the rating given to item i by user u, ru is the mean rating of all the ratings provided by u (Jannach et al. 2011, p. 19).”
News recommender systems - Survey and roads ahead (2018, background): “Recommender Systems have shown to be a valuable tool to help users in such situations of information overload [84].”
The Effect of Recommendations on Network Structure (2016, background): “For a thorough overview of this area, we point the interested reader to some relevant papers and surveys [1, 18, 19, 20], and the references therein.”
Reinforcement Learning based Recommender Systems: A Survey (2021, background): “, the system cannot provide useful recommendation when the user or item is new), lack of novelty and diversity, scalability, low quality recommendation, and great computational expense [2, 3, 12].”
Model-Based Purchase Predictions for Large Assortments (2016, background): “Many online retailers predict a customer’s next purchase using collaborative filtering algorithms, for example, by relying on counts of the co-occurrence of items in the purchase history data (Jannach et al., 2011; Liu et al., 2009).”